The evolution of the ideas in Finance has been huge in the last decades. Nowadays the financial markets offer investors sophisticated products. And investors in turn demand reliable financial instruments to meet their needs in search for greater returns and lower risks. This development is based mainly on asset pricing methodologies. The greatest part of this knowledge comes from the seminal works of Black and Scholes (1973) and Merton (1973). To summarize, their works are based on the assumption of a specific stochastic process that governs asset prices. And then a derivative of this underlying asset can be priced. The nature of the stochastic process that describes the evolution of prices is the key point for deriving pricing formulae. The analysis of the behavior of commodity prices has two approaches. The first approach considers prices as a consequence of the equilibrium between supply and demand. These models have not received enough attention in literature. The second approach, which has received more attention, is based on the analysis of price time series. The commodities have particular features because they are most of the times negotiated in future markets. The consequence is that the one factor models badly describe their stylized facts. The factors (stochastic variables) are known as state variables which most of the times are non observables, and need to be estimated. When state variables are Gaussians and the observation equation is linear in states, the classical Kalman filter can be used to access these variables. If non linearity is present extended Kalman filter is used, but when state variables are non Gaussian the literature does not use filtering processes. This thesis analyses the stochastic processes of commodities proposing extensions to the existing models. The derivation of models is based on Duffie and Kan (1996) transform, in a non arbitrage environment. Some extensions are non Gaussian. This thesis investigates the estimation of these models using particle filter methodology. The particle filter is a recursive procedure for integration in the sequential Monte-Carlo methods. The advantage of this methodology is that it does not require linear or Gaussian conditions. The contributions of this research are the extensions of stochastic processes that can be used for any commodity and the use of particle filter as an estimation methodology in Finance. Furthermore the thesis presents: (i) the conclusions about two factor models applied to oil prices; (ii) the analysis of the use of particle filter verifying that errors in both, Kalman filter and particle filter are close and that parameters estimation is in accordance with the literature; (iii) the analysis of the implementation of particle filter showing that it is viable considering the computational time of filtering and parameters estimation. The thesis concludes that the particle filter is viable, although time consuming, due to the hardware development. And more, since particle filter is useful for complex inference problems, its application to sophisticated models is promising.